PEAT-CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model.

Peatlands are poorly represented in global Earth system modeling frameworks. Here we add a peatland-specific land surface hydrology module (PEAT-CLSM) to the Catchment Land Surface Model (CLSM) of the NASA Goddard Earth Observing System (GEOS) framework. The amended TOPMODEL approach of the original CLSM that uses topography characteristics to model catchment processes is discarded, and a peatland-specific model concept is realized in its place. To facilitate its utilization in operational GEOS efforts, PEAT-CLSM uses the basic structure of CLSM and the same global input data. Parameters used in PEAT-CLSM are based on literature data. A suite of CLSM and PEAT-CLSM simulations for peatland areas between 40°N and 75°N is presented and evaluated against a newly compiled data set of groundwater table depth and eddy covariance observations of latent and sensible heat fluxes in natural and seminatural peatlands. CLSM's simulated groundwater tables are too deep and variable, whereas PEAT-CLSM simulates a mean groundwater table depth of -0.20 m (snow-free unfrozen period) with moderate temporal fluctuations (standard deviation of 0.10 m), in significantly better agreement with in situ observations. Relative to an operational CLSM version that simply includes peat as a soil class, the temporal correlation coefficient is increased on average by 0.16 and reaches 0.64 for bogs and 0.66 for fens when driven with global atmospheric forcing data. In PEAT-CLSM, runoff is increased on average by 38% and evapotranspiration is reduced by 19%. The evapotranspiration reduction constitutes a significant improvement relative to eddy covariance measurements.

Montane ecosystem productivity responds more to global circulation patterns than climatic trends.

Regional ecosystem productivity is highly sensitive to inter-annual climate variability, both within and outside the primary carbon uptake period. However, Earth system models lack sufficient spatial scales and ecosystem processes to resolve how these processes may change in a warming climate. Here, we show, how for the European Alps, mid-latitude Atlantic ocean winter circulation anomalies drive high-altitude summer forest and grassland productivity, through feedbacks among orographic wind circulation patterns, snowfall, winter and spring temperatures, and vegetation activity. Therefore, to understand future global climate change influence to regional ecosystem productivity, Earth systems models need to focus on improvements towards topographic downscaling of changes in regional atmospheric circulation patterns and to lagged responses in vegetation dynamics to non-growing season climate anomalies.

A primer for data assimilation with ecological models using Markov Chain Monte Carlo (MCMC).

Data assimilation, or the fusion of a mathematical model with ecological data, is rapidly expanding knowledge of ecological systems across multiple spatial and temporal scales. As the amount of ecological data available to a broader audience increases, quantitative proficiency with data assimilation tools and techniques will be an essential skill for ecological analysis in this data-rich era. We provide a data assimilation primer for the novice user by (1) reviewing data assimilation terminology and methodology, (2) showcasing a variety of data assimilation studies across the ecological, environmental, and atmospheric sciences with the aim of gaining an understanding of potential applications of data assimilation, and (3) applying data assimilation in specific ecological examples to determine the components of net ecosystem carbon uptake in a forest and also the population dynamics of the mayfly (Hexagenia limbata, Serville). The review and examples are then used to provide guiding principles to newly proficient data assimilation practitioners.

Discrimination of dairy industry isolates of the Lactobacillus casei group.

Lactobacilli are a major part of the microflora of the gut and of many fermented dairy products, and are found in a variety of environments. Lactobacillus casei, Lactobacillus paracasei, Lactobacillus rhamnosus, and Lactobacillus zeae form a closely related taxonomic group within the facultatively heterofermentative lactobacilli. The classification and nomenclature of these bacteria are controversial. In this study, relationships between these species were investigated using type strains and dairy industry isolates examined with DNA-based techniques and conventional carbohydrate use tests. Carbohydrate use patterns gave poor discrimination of some species, but DNA PCR using specific primers targeted to sequences of the 16S rRNA gene discriminated 4 types consistent with the currently recognized species. Pulsed-field agarose gel electrophoresis of chromosomal NotI restriction fragments identified 18 different band patterns from 21 independent Lactobacillus isolates and confirmed the identity of L. casei strains from 2 culture collections (CSCC 5203 and ASCC 290), both representing the type strain of L. casei. Some isolates were reclassified as L. rhamnosus, suggesting that the prevalence of L. rhamnosus as a natural component of the microflora of dairy foods and dairy environments has previously been underestimated. These methods can provide a practical basis for discrimination of the species and identification of individual industrial strains.

Progressive restriction in fate potential by neural progenitors during cerebral cortical development.

During early stages of cerebral cortical development, progenitor cells in the ventricular zone are multipotent, producing neurons of many layers over successive cell divisions. The laminar fate of their progeny depends on environmental cues to which the cells respond prior to mitosis. By the end of neurogenesis, however, progenitors are lineally committed to producing upper-layer neurons. Here we assess the laminar fate potential of progenitors at a middle stage of cortical development. The progenitors of layer 4 neurons were first transplanted into older brains in which layer 2/3 was being generated. The transplanted neurons adopted a laminar fate appropriate for the new environment (layer 2/3), revealing that layer 4 progenitors are multipotent. Mid-stage progenitors were then transplanted into a younger environment, in which layer 6 neurons were being generated. The transplanted neurons bypassed layer 6, revealing that layer 4 progenitors have a restricted fate potential and are incompetent to respond to environmental cues that trigger layer 6 production. Instead, the transplanted cells migrated to layer 4, the position typical of their origin, and also to layer 5, a position appropriate for neither the host nor the donor environment. Because layer 5 neurogenesis is complete by the stage that progenitors were removed for transplantation, restrictions in laminar fate potential must lag behind the final production of a cortical layer. These results suggest that a combination of intrinsic and environmental cues controls the competence of cortical progenitor cells to produce neurons of different layers.